[1]朱璇,袁彬,同元辉,等.致密低渗油藏压裂井网气驱深度学习预测模型[J].深圳大学学报理工版,2022,39(5):559-566.[doi:10.3724/SP.J.1249.2022.05559]
 ZHU Xuan,YUAN Bin,TONG Yuanhui,et al.Deep-learning-based proxy model for forecasting gas flooding performance of fractured well pattern in tight oil reservoirs[J].Journal of Shenzhen University Science and Engineering,2022,39(5):559-566.[doi:10.3724/SP.J.1249.2022.05559]
点击复制

致密低渗油藏压裂井网气驱深度学习预测模型()
分享到:

《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第39卷
期数:
2022年第5期
页码:
559-566
栏目:
环境与能源
出版日期:
2022-09-16

文章信息/Info

Title:
Deep-learning-based proxy model for forecasting gas flooding performance of fractured well pattern in tight oil reservoirs
文章编号:
202205010
作者:
朱璇1 袁彬1 同元辉2 赵明泽1 郑贺1 刘秀磊1
1)中国石油大学(华东)石油工程学院,山东青岛 266580
2)中国石油塔里木油田实验检测研究院油气分析测试中心,新疆库尔勒841009
Author(s):
ZHU Xuan1 YUAN Bin1 TONG Yuanhui2 ZHAO Mingze1 ZHENG He1 and LIU Xiulei1
1) School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong Province, P.R.China
2) Center of Oil & Gas Analysis, PetroChina Tarim Oilfield Experimental Testing Institute, Korla 841009, Xinjiang Uygur Autonomous Region, P.R.China
关键词:
油田开发致密油藏气驱压裂井网深度学习代理模型提高采收率水平井
Keywords:
oilfield development tight oil reservoir gas flooding fracture well pattern deep learning proxy model enhanced oil recovery horizontal well
分类号:
TE341;TE348
DOI:
10.3724/SP.J.1249.2022.05559
文献标志码:
A
摘要:
压裂井网气驱将油藏压裂与注气井网驱油结合,是当前致密低渗油藏提高采收率有效技术之一.水力裂缝及多相流动复杂性,使得基于精细油藏数值模拟的压裂井网气驱效果预测变得困难且耗时.提出一种基于均方根传播(root mean square propagation, RMSProp)深度学习的压裂井网气驱效果预测方法.通过建立压裂直井/水平井混合井网气驱数值模拟模型,引入高斯函数定量表征压裂水平井多级裂缝分布特征.利用正交试验筛选试验样本方案,自主编程实现数值模拟结果自动提取与数据处理,建立致密低渗油藏压裂井网气驱样本数据库.基于随机森林算法,筛选油藏地质、裂缝、生产等关键参数重要性特征,通过误差逆传播(back propagation, BP)神经网络、长短期记忆单元(long short-term memory, LSTM)、双向长短期记忆单元(bi-directional long short-term memory, BiLSTM)等深度学习算法,建立日产油、地层压力和采出程度预测代理模型,通过与油藏数值模拟对比,验证模型准确性.结果表明,BiLSTM算法在预测压裂井网气驱和压裂衰竭开发时效果最好.所提出的基于RMSProp的深度学习方法有效兼顾了模型实用性与精确性,为致密低渗油藏压裂井网气驱模拟预测提供了新途径.
Abstract:
Gas flooding in fractured well pattern is a combination of hydraulic fracturing and gas injection to displace reservoir oil within well pattern, and is one of the effective technologies for improving oil recovery in tight and low permeability reservoirs. However, due to the complexity of hydraulic fracturing and multi-phase flow, it becomes difficult and time-consuming to establish a fine reservoir numerical simulation to predict the performance of gas flooding in fractured well pattern. Therefore, we propose a deep learning method based on root mean square back propagation (RMSProp) for predicting the gas flooding effect of fracturing well pattern. Firstly, we establish a basic numerical simulation model of gas flooding in fractured vertical/horizontal hybrid well pattern in tight oil reservoir. Gaussian function curve is introduced to quantify the position of heel/toe of horizontal well and distributions of hydraulic fracture tips. The orthogonal test is then used to screen the test sample set, and the simulation results are auto-extracted and processed to build a database of gas flooding in tight oil reservoirs. The key parameters such as reservoir geology, fracture and production as input features are screened according to their importance in prediction by using the random forest algorithm. Then, we establish the prediction proxy models of daily oil production, reservoir pressure and field oil extraction (FOE) through deep learning algorithms such as back propagation (BP) neural network, long short-term memory (LSTM) and bi-directional long-short term memory (BiLSTM). Compared with the results of reservoir numerical simulation, the accuracy of the prediction proxy model is verified, and the results proves that the BiLSTM algorithm has the best performance in forecasting the gas flooding performance of fractured well pattern. The RMSprop-based deep learning method effectively takes into account the practicability and accuracy of the model andprovides a new approach to reservoir simulation and prediction of the fracturing well pattern gas flooding in tight and low permeability reservoirs.

参考文献/References:

[1] 常晓宇. 2021年国内外油气行业发展报告[R].北京:国家高端智库中国石油集团经济技术研究院,2021.
CHANG Xiaoyu. 2021 Domestic and foreign oil and gas industry development report [R]. Beijing: Institute of Economics and Technology of China National Petroleum Corporation, 2021.(in Chinese)
[2] 房平亮,冉启全,鞠斌山. 致密油藏压裂开发流固耦合数值模拟[J].中国矿业,2017,26(4):140-145.
FANG Pingliang, RAN Qiquan, JU Binshan. Coupled analysis of flow and deformation in tight oil reservoir fracturing and production simulation [J]. China Mining Magazine, 2017, 26 (4): 140-145.(in Chinese)
[3] LI Yuanjun, POPA A, JOHNSON A, et al. Dynamic layered pressure map generation in a mature waterflooding reservoir using artificial intelligence approach [C]// Proceedings of the SPE Western Regional Meeting. Garden Grove California, USA: Society of Petroleum Engineers, 2018: SPE-190042-MS. doi: 10.2118/190042-MS
[4] ARPAT B G, CAERS J, HAAS A. Characterization of west-africa submarine channel reservoirs: a neural network based approach to integration of seismic data [C]// Proceedings of the SPE Annual Technical Conference and Exhibition. New Orleans, Louisiana: Society of Petroleum Engineers, 2001: SPE-71345-MS. doi: 10.2118/71345-MS
[5] HEGEMAN P S, DONG C, VAROTSIS N, et al. Application of artificial neural networks to downhole fluid analysis [J]. SPE Reservoir Evaluation & Engineering, 2009, 12(1): 8-13.
[6] POPA A S, PATEL A. Neural networks for production curve pattern recognition applied to cyclic steam optimization in diatomite reservoirs [C]// Proceedings of the SPE Western Regional Meeting. Bakersfield. California, USA: Society of Petroleum Engineers, 2012: SPE-153185-MS. doi: 10.2118/153185-MS
[7] MAKHOTIN I, KOROTEEV D, BURNAEV E. Gradient boosting to boost the efficiency of hydraulic fracturing [J]. Journal of Petroleum Exploration and Production Technology. 2019, 9(3): 1919-1925.
[8] LIU Kailei, XU Boyue, KIM C et al. Well performance from numerical methods to machine learning approach: applications in multiple fractured shale reservoirs [J]. Geofluids, 2021, 2021: 1-13.doi:10.1155/2021/316945
[9] SONG Xuanyi, LIU Yuetian, XUE Liang, et al. Time-series well performance prediction based on long short-term memory (LSTM) neural network model [J]. Journal of Petroleum Science and Engineering. 2020, 186:106682.
[10] KALAM S, ABU-KHAMSIN S A, AL-YOUSEF H Y, et al. A novel empirical correlation for waterflooding performance prediction in stratified reservoirs using artificial intelligence [J]. Neural Computing and Applications. 2021, 33(7): 2497-2514.
[11] PAL M. On application of machine learning method for history matching and forecasting of times series data from hydrocarbon recovery process using water flooding [J]. Petroleum Science and Technology. 2021, 39(15/16):519-549.
[12] DONG Peng, LIAO Xinwei, CHEN Zhiming, et al. An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network [J]. Advances in Geo-Energy Research. 2019, 3(4): 355-364.
[13] 张烈辉,贾鸣,张芮菡,等.裂缝性油藏离散裂缝网络模型与数值模拟[J].西南石油大学学报自然科学版,2017,39(3):121-127.
ZHANG Liehui, JIA Ming, ZHANG Ruihan, et al. Discrete fracture network modeling and numerical simulation of fractured reservoirs [J]. Journal of Southwest Petroleum University Science and Technology Edition, 2017, 39 (3): 121-127.(in Chinese)
[14] 袁彬,苏玉亮,丰子泰,等.体积压裂水平井缝网渗流特征与产能分布研究[J].深圳大学学报理工版,2013,30(5):545-550.
YUAN Bin, SU Yuliang, FENG Zitai, et al. Productivity distribution and flow characteristics of volume-fractured horizontal wells [J]. Journal of Shenzhen University Science and Engineering, 2013, 30(5): 545-550.(in Chinese)
[15] 杨子清,陈文龙,杨军侠,等.分段压裂水平井裂缝形态优化及产能特征研究[J].石油天然气学报,2014,36(1):99-103.
YANG Ziqing, CHEN Wenlong, YANG Junxia, et al. The study on fracture morphology optimization and productivity characteristics of staged fractured horizontal wells [J]. Journal of Oil and Gas Technology, 2014, 36(1): 99-103.(in Chinese)
[16] 张剑.基于代理模型技术的高速列车性能参数设计及优化[D].成都:西南交通大学,2015.
ZHANG Jian. The high-speed train performance parameter design and optimization based on surrogate model technology [D]. Chengdu: Southwest Jiaotong University, 2015.(in Chinese)
[17] SIAMI-NAMINI S, TAVAKOLI N, NAMIN A S. The performance of LSTM and BiLSTM in forecasting time series [M]// BARU C, HUAN J, KHAN L, et al. IEEE International Conference on Big Data. 2019: 3285-3292
[18] CALVETTE T, GURWICZ A, ABREU A C, et al. Forecasting smart well production via deep learning and data driven optimization [C]// Proceedings of the Offshore Technology Conference Brasil. Rio de Janeiro, Brazil: Society of Petroleum Engineers, 2019: OTC-29861-MS. doi: 10.4043/29861-MS
[19] GOODFELLOW I, COURVILLE A. Deep learning [M]. [S.l.]: MIT Press, 2016.
[20] ALIYUDA K, HOWELL J, HUMPHREY E. Impact of geological variables in controlling oil-reservoir performance: an insight from a machine-learning technique [J]. SPE Reservoir Evaluation & Engineering, 2020, 23(4): 1314-1327.
[21] 王文东,石梦翮,庄新宇,等.基于机器学习的井位及注采参数联合优化方法[J].深圳大学学报理工版,2022,39(2):126-133.
WANG Wendong, SHI Menghe, ZHUANG Xinyu, et al. Joint optimization method of well location and injection-production parameters based on machine learning [J]. Journal of Shenzhen University Science and Engineering, 2022, 39(2): 126-133.(in Chinese)

相似文献/References:

[1]陈民锋,赵晶,赵梦盼,等.低渗透稠油油藏储量有效动用界限研究[J].深圳大学学报理工版,2013,30(No.2(111-220)):210.[doi:10.3724/SP.J.1249.2013.02210]
 Chen Minfeng,Zhao Jing,Zhao Mengpan,et al.Study on limits of effective drive in low-permeability heavy-oil reservoirs[J].Journal of Shenzhen University Science and Engineering,2013,30(5):210.[doi:10.3724/SP.J.1249.2013.02210]
[2]郭和坤,刘强,李海波,等.四川盆地侏罗系致密储层孔隙结构特征[J].深圳大学学报理工版,2013,30(No.3(221-330)):306.[doi:10.3724/SP.J.1249.2013.03306]
 Guo Hekun,Liu Qiang,Li Haibo,et al.Microstructural characteristics of the Jurassic tight oil reservoirs in Sichuan Basin[J].Journal of Shenzhen University Science and Engineering,2013,30(5):306.[doi:10.3724/SP.J.1249.2013.03306]
[3]袁彬,苏玉亮,丰子泰,等.体积压裂水平井缝网渗流特征与产能分布研究[J].深圳大学学报理工版,2013,30(No.5(441-550)):545.[doi:10.3724/SP.J.1249.2013.05545]
 Yuan Bin,Su Yuliang,Feng Zitai,et al.Productivity distribution and flow characteristics of volume-fractured horizontal wells[J].Journal of Shenzhen University Science and Engineering,2013,30(5):545.[doi:10.3724/SP.J.1249.2013.05545]
[4]陈民锋,李晓风,王敏,等.深水油田高饱和油藏能量合理补充时机研究[J].深圳大学学报理工版,2013,30(No.6(551-660)):649.[doi:10.3724/SP.J.1249.2013.06649]
 Chen Minfeng,Li Xiaofeng,Wang Min,et al.Reasonable opportune moment of energy supplement of high saturation reservoirs in deepwater oilfield[J].Journal of Shenzhen University Science and Engineering,2013,30(5):649.[doi:10.3724/SP.J.1249.2013.06649]
[5]廉培庆,陈志海,董广为,等.水平井与非均质盒式油藏耦合模型[J].深圳大学学报理工版,2015,32(3):266.[doi:10.3724/SP.J.1249.2015.03266]
 Lian Peiqing,Chen Zhihai,Dong Guangwei,et al.A coupling model for horizontal well in heterogeneous box-shaped reservoir[J].Journal of Shenzhen University Science and Engineering,2015,32(5):266.[doi:10.3724/SP.J.1249.2015.03266]
[6]李帅,丁云宏,刘广峰,等.致密储层体积改造润湿反转提高采收率的研究[J].深圳大学学报理工版,2017,34(1):98.[doi:10.3724/SP.J.1249.2017.01098]
 Li Shuai,Ding Yunhong,Liu Guangfeng,et al.Enhancing oil recovery by wettability alteration during fracturing in tight reservoirs[J].Journal of Shenzhen University Science and Engineering,2017,34(5):98.[doi:10.3724/SP.J.1249.2017.01098]
[7]陈民锋,王兆琪,张琪琛,等.启动压力影响下注采井间有效驱替规律[J].深圳大学学报理工版,2017,34(1):91.[doi:10.3724/SP.J.1249.2017.01091]
 Chen Minfeng,Wang Zhaoqi,Zhang Qichen,et al.Effective displacement rules for interwell with threshold pressure[J].Journal of Shenzhen University Science and Engineering,2017,34(5):91.[doi:10.3724/SP.J.1249.2017.01091]
[8]赵振峰,唐梅荣,杜现飞,等.压裂水平井非稳态产能分析与影响因素研究——以鄂尔多斯长庆致密油为例[J].深圳大学学报理工版,2017,34(6):647.[doi:10.3724/SP.J.1249.2017.06647]
 Zhao Zhenfeng,Tang Meirong,Du Xianfei,et al.Factors affecting rate transient of fractured horizontal well in tight oil reservoir——Erdos Basin Changqing tight oil[J].Journal of Shenzhen University Science and Engineering,2017,34(5):647.[doi:10.3724/SP.J.1249.2017.06647]
[9]张贤松,谢晓庆,康晓东,等.非均质油藏聚合物驱注入参数优化方法改进与应用[J].深圳大学学报理工版,2018,35(4):362.[doi:10.3724/SP.J.1249.2018.04362]
 ZHANG Xiansong,XIE Xiaoqing,KANG Xiaodong,et al.An improved optimization method and application for injection parameter of polymer flooding for heterogeneous reservoir[J].Journal of Shenzhen University Science and Engineering,2018,35(5):362.[doi:10.3724/SP.J.1249.2018.04362]
[10]张继成,范佳乐,匡力,等.一种预测特高含水期开发指标的联解法[J].深圳大学学报理工版,2018,35(6):558.[doi:10.3724/SP.J.1249.2018.06574]
 ZHANG Jicheng,FAN Jiale,KUANG Li,et al.An integrated method for predicting the development index of extra-high water cut period[J].Journal of Shenzhen University Science and Engineering,2018,35(5):558.[doi:10.3724/SP.J.1249.2018.06574]
[11]毛新军,胡广文,张晓文,等.双重介质致密油藏油水两相瞬态流动模拟方法[J].深圳大学学报理工版,2021,38(6):572.[doi:10.3724/SP.J.1249.2021.06572]
 MAO Xinjun,HU Guangwen,ZHANG Xiaowen,et al.Simulation method of oil-water two-phase transient flow in dual-porosity system in tight reservoir[J].Journal of Shenzhen University Science and Engineering,2021,38(5):572.[doi:10.3724/SP.J.1249.2021.06572]

备注/Memo

备注/Memo:
Received: 2022- 04-22; Accepted: 2022-06-27; Online (CNKI): 2022-08-09
Foundation: National Natural Science Foundation of China (52074338);National Key R & D Program of China (2019YFA0708700); Shandong Mountain Tai Scholar Program (ZX20210178)
Corresponding author: Professor YUAN Bin. E-mail: yuanbin@upc.edu.cn
Citation: ZHU Xuan, YUAN Bin, TONG Yuanhui, et al. Deep-learning-based proxy model for forecasting gas flooding performance of fractured well pattern in tight oil reservoirs [J]. Journal of Shenzhen University Science and Engineering, 2022, 39(5): 559-566.(in Chinese)
基金项目:国家自然科学基金资助项目(52074338);国家重点研发计划资助项目(2019YFA0708700);泰山学者建设工程专项经费资助项目(ZX20210178);中央高校基本科研业务费专项资金资助项目(20CX06071A)
作者简介:朱璇(1999—),中国石油大学(华东)硕士研究生.研究方向:油气田开发工程. E-mail: s21020110@s.upc.edu.cn
袁彬(1988—),中国石油大学(华东)教授、博士生导师.研究方向:渗流理论与油气藏开发工程、油气工程信息与智能技术. E-mail: yuanbin@upc.edu.cn
引文:朱璇,袁彬,同元辉,等.致密低渗油藏压裂井网气驱深度学习预测模型[J].深圳大学学报理工版,2022,39(5):559-566.
更新日期/Last Update: 2022-09-30